Overview

Dataset statistics

Number of variables11
Number of observations164
Missing cells31
Missing cells (%)1.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.2 KiB
Average record size in memory88.8 B

Variable types

NUM9
CAT2

Warnings

Density has 4 (2.4%) missing values Missing
GDP has 12 (7.3%) missing values Missing
GDP per cap has 12 (7.3%) missing values Missing
Temperature has 3 (1.8%) missing values Missing
df_index has unique values Unique
Country name has unique values Unique
Normalized cases has unique values Unique
Population has unique values Unique
Mortality rate has 12 (7.3%) zeros Zeros
Normalized deaths has 12 (7.3%) zeros Zeros

Reproduction

Analysis started2020-10-14 22:26:41.767910
Analysis finished2020-10-14 22:27:17.585462
Duration35.82 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.43292683
Minimum0
Maximum181
Zeros1
Zeros (%)0.6%
Memory size1.3 KiB
2020-10-14T23:27:17.841024image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.15
Q147.75
median89.5
Q3137.25
95-th percentile171.85
Maximum181
Range181
Interquartile range (IQR)89.5

Descriptive statistics

Standard deviation52.25810544
Coefficient of variation (CV)0.5715458014
Kurtosis-1.190680585
Mean91.43292683
Median Absolute Deviation (MAD)45
Skewness0.02736182022
Sum14995
Variance2730.909584
MonotocityStrictly increasing
2020-10-14T23:27:18.134068image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
18110.6%
 
6710.6%
 
6510.6%
 
6410.6%
 
6310.6%
 
6210.6%
 
6110.6%
 
6010.6%
 
5910.6%
 
5810.6%
 
Other values (154)15493.9%
 
ValueCountFrequency (%) 
010.6%
 
110.6%
 
210.6%
 
410.6%
 
710.6%
 
ValueCountFrequency (%) 
18110.6%
 
18010.6%
 
17910.6%
 
17810.6%
 
17710.6%
 

Country name
Categorical

UNIQUE

Distinct164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
Zimbabwe
 
1
Djibouti
 
1
Congo, Rep.
 
1
Malaysia
 
1
New Zealand
 
1
Other values (159)
159 
ValueCountFrequency (%) 
Zimbabwe10.6%
 
Djibouti10.6%
 
Congo, Rep.10.6%
 
Malaysia10.6%
 
New Zealand10.6%
 
Uganda10.6%
 
Myanmar10.6%
 
Russian Federation10.6%
 
Turkey10.6%
 
Belarus10.6%
 
Other values (154)15493.9%
 
2020-10-14T23:27:18.438858image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique164 ?
Unique (%)100.0%
2020-10-14T23:27:18.657985image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length30
Median length8
Mean length9.201219512
Min length4

Mortality rate
Real number (ℝ≥0)

ZEROS

Distinct153
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.601611255
Minimum0
Maximum28.91625616
Zeros12
Zeros (%)7.3%
Memory size1.3 KiB
2020-10-14T23:27:18.880254image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.120588135
median1.99068027
Q33.096776389
95-th percentile6.234152212
Maximum28.91625616
Range28.91625616
Interquartile range (IQR)1.976188254

Descriptive statistics

Standard deviation2.915400699
Coefficient of variation (CV)1.120613502
Kurtosis40.84700915
Mean2.601611255
Median Absolute Deviation (MAD)1.017159386
Skewness5.104675012
Sum426.6642458
Variance8.499561234
MonotocityNot monotonic
2020-10-14T23:27:19.087456image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0127.3%
 
6.05644794210.6%
 
4.22077922110.6%
 
3.70738926310.6%
 
1.7710710610.6%
 
2.217294910.6%
 
2.90691536210.6%
 
0.56451612910.6%
 
2.24646983310.6%
 
0.83402835710.6%
 
Other values (143)14387.2%
 
ValueCountFrequency (%) 
0127.3%
 
0.0468059287510.6%
 
0.20618556710.6%
 
0.384467512510.6%
 
0.388175574810.6%
 
ValueCountFrequency (%) 
28.9162561610.6%
 
11.6252953510.6%
 
10.495559810.6%
 
9.74023718410.6%
 
8.84196342310.6%
 

Normalized cases
Real number (ℝ≥0)

UNIQUE

Distinct164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003138400528
Minimum3.208054169e-06
Maximum0.01521941358
Zeros0
Zeros (%)0.0%
Memory size1.3 KiB
2020-10-14T23:27:19.309082image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum3.208054169e-06
5-th percentile5.2630786e-05
Q10.0003695141644
median0.001530368924
Q30.005117437358
95-th percentile0.009808820373
Maximum0.01521941358
Range0.01521620552
Interquartile range (IQR)0.004747923194

Descriptive statistics

Standard deviation0.003418165608
Coefficient of variation (CV)1.089142567
Kurtosis0.9438946883
Mean0.003138400528
Median Absolute Deviation (MAD)0.001395941973
Skewness1.242360753
Sum0.5146976866
Variance1.168385612e-05
MonotocityNot monotonic
2020-10-14T23:27:19.530000image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.000236553489510.6%
 
0.00160764112910.6%
 
0.00538959039110.6%
 
0.0132812600810.6%
 
0.000532792806610.6%
 
0.00646099864310.6%
 
0.00398021892610.6%
 
0.00423610594410.6%
 
6.471705605e-0510.6%
 
0.00432073718510.6%
 
Other values (154)15493.9%
 
ValueCountFrequency (%) 
3.208054169e-0610.6%
 
8.775035551e-0610.6%
 
1.108207196e-0510.6%
 
1.674092724e-0510.6%
 
2.087974889e-0510.6%
 
ValueCountFrequency (%) 
0.0152194135810.6%
 
0.0142740226410.6%
 
0.0132812600810.6%
 
0.0116034777810.6%
 
0.0114330221810.6%
 

Normalized deaths
Real number (ℝ≥0)

ZEROS

Distinct153
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.010595813e-05
Minimum0
Maximum0.0008685085538
Zeros12
Zeros (%)7.3%
Memory size1.3 KiB
2020-10-14T23:27:20.067891image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.846155225e-06
median2.8732307e-05
Q39.234305459e-05
95-th percentile0.0004570041216
Maximum0.0008685085538
Range0.0008685085538
Interquartile range (IQR)8.549689936e-05

Descriptive statistics

Standard deviation0.0001549799337
Coefficient of variation (CV)1.719974316
Kurtosis8.106901404
Mean9.010595813e-05
Median Absolute Deviation (MAD)2.657157096e-05
Skewness2.803093656
Sum0.01477737713
Variance2.401877986e-08
MonotocityNot monotonic
2020-10-14T23:27:20.270326image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0127.3%
 
1.225201636e-0510.6%
 
2.755718633e-0610.6%
 
0.000113105939310.6%
 
2.767683421e-0510.6%
 
5.01801827e-0510.6%
 
8.984986084e-0510.6%
 
8.672590624e-0810.6%
 
6.923849199e-0610.6%
 
5.089558215e-0610.6%
 
Other values (143)14387.2%
 
ValueCountFrequency (%) 
0127.3%
 
8.672590624e-0810.6%
 
3.620348656e-0710.6%
 
3.628367807e-0710.6%
 
5.962482227e-0710.6%
 
ValueCountFrequency (%) 
0.000868508553810.6%
 
0.000679921133310.6%
 
0.000663426838810.6%
 
0.00064885572210.6%
 
0.000629316592310.6%
 

Population
Real number (ℝ≥0)

UNIQUE

Distinct164
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41962453.03
Minimum38019
Maximum1397715000
Zeros0
Zeros (%)0.0%
Memory size1.3 KiB
2020-10-14T23:27:20.475865image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum38019
5-th percentile187629.9
Q12837354.25
median10062506
Q330378991
95-th percentile124137000.8
Maximum1397715000
Range1397676981
Interquartile range (IQR)27541636.75

Descriptive statistics

Standard deviation154968347.2
Coefficient of variation (CV)3.693024025
Kurtosis68.24629181
Mean41962453.03
Median Absolute Deviation (MAD)8752651.5
Skewness8.071452003
Sum6881842297
Variance2.401518863e+16
MonotocityNot monotonic
2020-10-14T23:27:20.683994image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
132659010.6%
 
932101810.6%
 
1169471910.6%
 
8291390610.6%
 
1106211310.6%
 
139497310.6%
 
1126307710.6%
 
4305305410.6%
 
677745210.6%
 
12626493110.6%
 
Other values (154)15493.9%
 
ValueCountFrequency (%) 
3801910.6%
 
3896410.6%
 
5282310.6%
 
7180810.6%
 
9711810.6%
 
ValueCountFrequency (%) 
139771500010.6%
 
136641775410.6%
 
27062556810.6%
 
21656531810.6%
 
20096359910.6%
 

Density
Real number (ℝ≥0)

MISSING

Distinct160
Distinct (%)100.0%
Missing4
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean196.6975754
Minimum2.040608667
Maximum7952.998418
Zeros0
Zeros (%)0.0%
Memory size1.3 KiB
2020-10-14T23:27:20.907639image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum2.040608667
5-th percentile4.070300071
Q137.46254922
median83.9494918
Q3197.3783688
95-th percentile512.3700563
Maximum7952.998418
Range7950.95781
Interquartile range (IQR)159.9158196

Descriptive statistics

Standard deviation648.9721845
Coefficient of variation (CV)3.299340031
Kurtosis130.3715878
Mean196.6975754
Median Absolute Deviation (MAD)53.86764375
Skewness10.95930711
Sum31471.61206
Variance421164.8963
MonotocityNot monotonic
2020-10-14T23:27:21.121749image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
106.960128910.6%
 
66.0576162810.6%
 
623.301970410.6%
 
219.978575910.6%
 
309.881467210.6%
 
93.6771974410.6%
 
63.579078810.6%
 
69.4378129110.6%
 
113.285586510.6%
 
46.6657397510.6%
 
Other values (150)15091.5%
 
(Missing)42.4%
 
ValueCountFrequency (%) 
2.04060866710.6%
 
2.97374558210.6%
 
3.2478709110.6%
 
3.51841396510.6%
 
3.6922510.6%
 
ValueCountFrequency (%) 
7952.99841810.6%
 
1514.4687510.6%
 
1239.57931210.6%
 
758.984551510.6%
 
669.494134910.6%
 

Income group
Categorical

Distinct4
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
High income
49 
Lower middle income
45 
Upper middle income
42 
Low income
28 
ValueCountFrequency (%) 
High income4929.9%
 
Lower middle income4527.4%
 
Upper middle income4225.6%
 
Low income2817.1%
 
2020-10-14T23:27:21.334360image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-14T23:27:21.449945image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:21.580741image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length19
Median length19
Mean length15.07317073
Min length10

GDP
Real number (ℝ≥0)

MISSING

Distinct152
Distinct (%)100.0%
Missing12
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean3.927697049e+11
Minimum429016605.2
Maximum1.434290284e+13
Zeros0
Zeros (%)0.0%
Memory size1.3 KiB
2020-10-14T23:27:21.763155image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum429016605.2
5-th percentile1687456820
Q11.279395585e+10
median3.984651639e+10
Q32.473862951e+11
95-th percentile1.716323652e+12
Maximum1.434290284e+13
Range1.434247383e+13
Interquartile range (IQR)2.345923393e+11

Descriptive statistics

Standard deviation1.342447748e+12
Coefficient of variation (CV)3.417900443
Kurtosis78.87985464
Mean3.927697049e+11
Median Absolute Deviation (MAD)3.607218915e+10
Skewness8.072374773
Sum5.970099514e+13
Variance1.802165957e+24
MonotocityNot monotonic
2020-10-14T23:27:21.979429image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.61076624e+1010.6%
 
2.827113185e+1210.6%
 
1.269482339e+1010.6%
 
4.211422679e+1110.6%
 
4.463147395e+1110.6%
 
6.698363422e+1010.6%
 
212245063010.6%
 
394147431110.6%
 
1.131495134e+1010.6%
 
1.801617412e+1110.6%
 
Other values (142)14286.6%
 
(Missing)127.3%
 
ValueCountFrequency (%) 
429016605.210.6%
 
596033333.310.6%
 
825385185.210.6%
 
105099259310.6%
 
118572867710.6%
 
ValueCountFrequency (%) 
1.434290284e+1310.6%
 
5.081769542e+1210.6%
 
3.845630031e+1210.6%
 
2.875142315e+1210.6%
 
2.827113185e+1210.6%
 

GDP per cap
Real number (ℝ≥0)

MISSING

Distinct152
Distinct (%)100.0%
Missing12
Missing (%)7.3%
Infinite0
Infinite (%)0.0%
Mean21040.9458
Minimum782.8165888
Maximum121292.7393
Zeros0
Zeros (%)0.0%
Memory size1.3 KiB
2020-10-14T23:27:22.198578image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum782.8165888
5-th percentile1654.432159
Q14806.334614
median13104.71408
Q331600.67621
95-th percentile59934.2016
Maximum121292.7393
Range120509.9227
Interquartile range (IQR)26794.3416

Descriptive statistics

Standard deviation21699.03686
Coefficient of variation (CV)1.031276686
Kurtosis3.435196544
Mean21040.9458
Median Absolute Deviation (MAD)9696.450238
Skewness1.682648514
Sum3198223.761
Variance470848200.5
MonotocityNot monotonic
2020-10-14T23:27:22.418573image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
5355.2701910.6%
 
9086.06069610.6%
 
2423.82876510.6%
 
59110.5625610.6%
 
3252.54637110.6%
 
17956.105410.6%
 
13620.1184810.6%
 
31399.4156710.6%
 
15636.5537610.6%
 
43235.7175710.6%
 
Other values (142)14286.6%
 
(Missing)127.3%
 
ValueCountFrequency (%) 
782.816588810.6%
 
984.028049710.6%
 
1103.64361610.6%
 
1143.45322510.6%
 
1269.60139910.6%
 
ValueCountFrequency (%) 
121292.739310.6%
 
101375.775310.6%
 
88240.9010310.6%
 
70989.2581310.6%
 
69900.8778510.6%
 

Temperature
Real number (ℝ)

MISSING

Distinct135
Distinct (%)83.9%
Missing3
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean18.56236025
Minimum-5.1
Maximum28.29
Zeros0
Zeros (%)0.0%
Memory size1.3 KiB
2020-10-14T23:27:22.631529image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Quantile statistics

Minimum-5.1
5-th percentile5.1
Q110.9
median21.9
Q325.3
95-th percentile27.15
Maximum28.29
Range33.39
Interquartile range (IQR)14.4

Descriptive statistics

Standard deviation8.216405275
Coefficient of variation (CV)0.4426379601
Kurtosis-0.7339420296
Mean18.56236025
Median Absolute Deviation (MAD)4.55
Skewness-0.7333183794
Sum2988.54
Variance67.50931564
MonotocityNot monotonic
2020-10-14T23:27:22.849460image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
10.5531.8%
 
24.931.8%
 
24.5531.8%
 
2631.8%
 
21.431.8%
 
27.1531.8%
 
26.831.8%
 
25.521.2%
 
22.3521.2%
 
24.4521.2%
 
Other values (125)13481.7%
 
(Missing)31.8%
 
ValueCountFrequency (%) 
-5.110.6%
 
-0.710.6%
 
1.510.6%
 
1.5510.6%
 
1.710.6%
 
ValueCountFrequency (%) 
28.2910.6%
 
28.2510.6%
 
2810.6%
 
27.8510.6%
 
27.6510.6%
 

Interactions

2020-10-14T23:27:03.004067image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:03.170744image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:03.339695image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:03.532091image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:03.679728image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:03.895296image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:04.052156image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:04.225166image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:04.404727image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:04.588967image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:04.833465image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:05.007680image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:05.170579image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:05.310050image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:05.463723image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:05.654944image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:05.823103image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:06.007090image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:06.176567image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:06.314862image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:06.462367image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:06.667461image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:06.822418image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:06.948537image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:07.093662image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:07.516544image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:07.676046image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:07.854390image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:08.002417image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:08.168014image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-14T23:27:09.410823image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-14T23:27:10.876844image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:11.033921image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
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2020-10-14T23:27:16.146967image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Correlations

2020-10-14T23:27:23.067508image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-14T23:27:23.348909image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-14T23:27:23.559099image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-14T23:27:23.798571image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-14T23:27:16.452465image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:16.876050image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:17.167180image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/
2020-10-14T23:27:17.363806image/svg+xmlMatplotlib v3.3.0, https://matplotlib.org/

Sample

First rows

df_indexCountry nameMortality rateNormalized casesNormalized deathsPopulationDensityIncome groupGDPGDP per capTemperature
00Afghanistan3.7073890.0010303.819487e-0538041754.056.937760Low income1.910135e+102293.55168412.60
11Angola3.6601030.0001475.373085e-0631825295.024.713052Lower middle income9.463542e+106929.67815821.55
22Albania2.8510610.0046081.313857e-042854191.0104.612263Upper middle income1.527808e+1014495.07851411.40
34United Arab Emirates0.4535520.0092754.206528e-059770529.0135.609110High income4.211423e+1169900.87784827.00
47Antigua and Barbuda3.0612240.0010093.089026e-0597118.0218.831818High income1.727759e+0922816.45220226.00
58Australia3.2248520.0010663.437902e-0525364307.03.247871High income1.392681e+1253320.26904321.65
69Austria1.8643100.0047558.865541e-058877067.0107.127967High income4.463147e+1159110.5625596.35
710Azerbaijan1.4663490.0039805.836391e-0510023318.0120.234317Upper middle income4.804765e+1015000.81649711.95
811Burundi0.2061860.0000428.672591e-0811530580.0435.178271Low income3.012335e+09782.81658919.80
912Belgium8.8419630.0098238.685086e-0411484055.0377.379590High income5.296067e+1154545.1508859.55

Last rows

df_indexCountry nameMortality rateNormalized casesNormalized deathsPopulationDensityIncome groupGDPGDP per capTemperature
154171Ukraine1.9883730.0045198.984986e-0544385155.077.029671Lower middle income1.537811e+1113341.2105198.30
155172Uruguay2.3523520.0005771.357701e-053461734.019.708028High income5.604591e+1022454.65794317.55
156174Uzbekistan0.8245320.0016321.346013e-0533580650.077.470851Lower middle income5.792129e+107288.76562612.05
157175St. Vincent and the Grenadines0.0000000.0005790.000000e+00110589.0282.589744Upper middle income8.253852e+0812982.89638926.80
158176Venezuela, RB0.8340280.0025232.104095e-0528515829.032.730792Upper middle incomeNaNNaN25.35
159177Vietnam3.2740880.0000113.628368e-0796462106.0308.125246Lower middle income2.619212e+118374.44432824.45
160178Yemen, Rep.28.9162560.0000702.012899e-0529161922.053.977853Low incomeNaNNaN23.85
161179South Africa2.4460120.0114332.796531e-0458558270.047.630120Upper middle income3.514316e+1112999.12025617.75
162180Zambia2.2721050.0008181.858795e-0517861030.023.341479Lower middle income2.306472e+103623.69939521.40
163181Zimbabwe2.9091380.0005331.549968e-0514645468.037.324591Lower middle income2.144076e+102953.48411321.00